SAR image segmentation based on Markov random field model and multiscale technology

Authors:
Xu Jiao;Xian-Bin Wen
Affiliations:
Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China and Tianjin Key Laboratory of Intelligence, Computing & Novel Software Tec ...;Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China and Tianjin Key Laboratory of Intelligence, Computing & Novel Software Tec ...
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

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Abstract

A valid multiscale classification method of synthetic aperture radar (SAR) imagery is proposed based on Multiscale technology and Markov Random Field (MRF) mode. Firstly, we employ multiscale autoregressive model for extracting the feature of SAR image. which is modeled by Markov Random Field (MRF) Model that relies on the Gaussian distribution. Secondly, using the joint probability distribution in terms of an energy function, estimation of parameters can be performed by the stochastic relaxation algorithm. Then the maximum posteriori (MAP) is designed as the optimal criterion and the final labels are obtained by the simulated annealing algorithm. Experimental results show that this method is accurate, efficient and robust.